17 citations · 32 across the 6 of their papers we have counts for
3 papers · 1 filter
Towards a learning-based performance modeling for accelerating Deep Neural Networks
Damiano Perri, Paolo Sylos Labini, Osvaldo Gervasi +2
Emerging applications such as Deep Learning are often data-driven, thus traditional approaches based on auto-tuners are not performance effective across the wide range of inputs us…
ProbGraph: High-Performance and High-Accuracy Graph Mining with Probabilistic Set Representations
Maciej Besta, Cesare Miglioli, Paolo Sylos Labini +11
Important graph mining problems such as Clustering are computationally demanding. To significantly accelerate these problems, we propose ProbGraph: a graph representation that enab…
Blocking Techniques for Sparse Matrix Multiplication on Tensor Accelerators
Paolo Sylos Labini, Massimo Bernaschi, Francesco Silvestri +1
Tensor accelerators have gained popularity because they provide a cheap and efficient solution for speeding up computational-expensive tasks in Deep Learning and, more recently, in…